Deep learning-based image recognition method and apparatus

Abstract

A deep learning-based image recognition method and apparatus are disclosed. The deep learning-based image recognition method comprises: training deep learning models based on deep learning frameworks, by using training image data, to obtain at least two deep learning models; selecting a predetermined number of deep learning models from the obtained deep learning models in a descending order of their recognition accuracies for verification image data, wherein the predetermined number is less than or equal to the number of the obtained deep learning models; and recognizing image data to be recognized using at least one of the selected deep learning models.

I/We claim: 
     
         1 . A deep learning-based image recognition method, comprising:
 training deep learning models based on deep learning frameworks by using training image data, to obtain at least two deep learning models;   selecting a predetermined number of deep learning models from the obtained deep learning models in a descending order of their recognition accuracies for verification image data, wherein the predetermined number is less than or equal to a number of the obtained deep learning models; and   recognizing image data to be recognized using at least one of the selected deep learning models.   
     
     
         2 . The method according to  claim 1 , further comprising:
 performing image preprocessing on at least one of the training image data, the verification image data, and the image data to be recognized.   
     
     
         3 . The method according to  claim 2 , wherein the image preprocessing comprises at least one of:
 random cropping, rotation, flipping, brightness adjustment, and contrast adjustment.   
     
     
         4 . The method according to  claim 3 , further comprising:
 storing preprocessed image data in a pre-established memory database.   
     
     
         5 . The method according to  claim 1 , wherein recognizing image data to be recognized using at least one of the selected deep learning models comprises:
 providing the selected deep learning models to a user; and   obtaining a deep learning model selected by the user, and recognizing the image data to be recognized by using the deep learning model selected by the user.   
     
     
         6 . The method according to  claim 1 , wherein training deep learning models based on deep learning frameworks by using training image data to obtain at least two deep learning models comprises:
 pushing state information for the training process to a user.   
     
     
         7 . The method according to  claim 1 , wherein training deep learning models based on deep learning frameworks by using training image data to obtain at least two deep learning models comprises drawing, in real time, a performance curve for a deep learning model which is being trained currently by using a web application programming interface; and the method further comprises:
 presenting the drawn performance curve.   
     
     
         8 . The method according to  claim 1 , wherein a number of the deep learning frameworks is equal to or greater than two. 
     
     
         9 . A deep learning-based image recognition apparatus, comprising:
 a processor; and   a memory having instructions stored thereon, which, when executed by the processor, cause the processor to:
 train deep learning models based on deep learning frameworks, by using training image data, to obtain at least two deep learning models; 
 select a predetermined number of deep learning models from the obtained deep learning models in a descending order of their recognition accuracies for verification image data, wherein the predetermined number is less than or equal to a number of the obtained deep learning models; and 
 recognize image data to be recognized using at least one of the selected deep learning models. 
   
     
     
         10 . The apparatus according to  claim 9 , wherein the instructions, when executed by the processor, further cause the processor to:
 perform image preprocessing on at least one of the training image data, the verification image data, and the image data to be recognized.   
     
     
         11 . The apparatus according to  claim 10 , wherein the image preprocessing comprises at least one of:
 random cropping, rotation, flipping, brightness adjustment, and contrast adjustment.   
     
     
         12 . The apparatus according to  claim 9 , wherein the instructions, when executed by the processor, further cause the processor to:
 establish a memory database; and   store preprocessed image data in the pre-established memory database.   
     
     
         13 . The apparatus according to  claim 9 , wherein the instructions, when executed by the processor, further cause the processor to:
 provide the selected deep training models to a user; and   obtain a deep learning model selected by the user, and recognize the image data to be recognized by using the deep learning model selected by the user.   
     
     
         14 . The apparatus according to  claim 9 , wherein the instructions, when executed by the processor, further cause the processor to:
 in the process of training deep learning models based on deep learning frameworks by using training image data to obtain at least two deep learning models, push state information for the training process to a user.   
     
     
         15 . The apparatus according to  claim 9 , wherein the instructions, when executed by the processor, further cause the processor to:
 in the process of training deep learning models based on deep learning frameworks by using training image data to obtain at least two deep learning models, draw, in real time, a performance curve for a deep learning model which is being trained currently by using a web application programming interface; and   present the drawn performance curve.   
     
     
         16 . The apparatus according to  claim 9 , wherein a number of the deep learning frameworks is equal to or greater than two. 
     
     
         17 . A non-transitory computer-readable storage medium having computer programs stored thereon, which, when executed by a processor, cause the processor to perform the method according to  claim 1 .